All of these questions need to be considered before the review begins, to ensure that the outcome is satisfactory. Advances in Technology and the Need for Greater Regulation Aside from the external factors that have made forecasting more challenging, namely the global pandemic and war in Ukraine, rapid advances in technology have also raised questions. The increased adoption of artificial intelligence (AI) and machine learning (ML) means that forecasting and risk models are now able to evolve much faster than they previously would have done. Without the right technology in place, this can soon start to create challenges. This situation brings to the fore the importance of good model management, especially given the economic turbulence witnessed in recent years. If the models relied upon by the financial services sector are no longer able to accurately forecast events - such as interest rate rises – then economic stability becomes much harder to maintain. For this independent review to be deemed a success, a structured approach must be taken, using a risk and control audit methodology and the use of consistent, robust and scalable analytics techniques. Here are some of the key elements that should be considered. Ensuring Good Governance It’s important to point out that forecasting and risk models are only as good as the governance framework in which they operate. No matter the quality of the data that goes in, if organisations are not continually reviewing their processes around model development, usage and reporting, there is a chance that these models become unfit for purpose. A clear governance framework will also help to ensure that any models requiring amendments or recalibration are easily and quickly identified. With automated modelling techniques now becoming far more common, organisations such as the Bank of England need to ensure that their forecasting and risk models are fully explainable. This becomes all the more important when faced with criticism or scrutiny from regulators or MPs. Of course, the Bank of England has thousands of models in place so questions will need to be asked around how broadly they want to consider their models, how in-depth they want to go and whether or not they want to review or rebuild every model. Similarly, there is a question around how far back into the data management space the review ought to go. When looking at risk mitigation, the auditors will also be focused on the controls in place to mitigate risk, whether or not they have been effective to date and if they remain fit for purpose. If the risk mitigation process is found to be overly manual or overly automated, this will raise questions about its effectiveness. If the risk mitigation process is found to be overly manual or overly automated, this will raise questions about its effectiveness. Banking & Financial Services 36 Finance Monthly.